↓ Skip to main content

PLOS

Modeling the Role of Negative Cooperativity in Metabolic Regulation and Homeostasis

Overview of attention for article published in PLOS ONE, November 2012
Altmetric Badge

Mentioned by

twitter
1 X user
facebook
1 Facebook page

Citations

dimensions_citation
16 Dimensions

Readers on

mendeley
35 Mendeley
Title
Modeling the Role of Negative Cooperativity in Metabolic Regulation and Homeostasis
Published in
PLOS ONE, November 2012
DOI 10.1371/journal.pone.0048920
Pubmed ID
Authors

Eliot C. Bush, Anne E. Clark, Chris M. DeBoever, Lillian E. Haynes, Sidra Hussain, Singer Ma, Matthew M. McDermott, Adam M. Novak, John S. Wentworth

Abstract

A significant proportion of enzymes display cooperativity in binding ligand molecules, and such effects have an important impact on metabolic regulation. This is easiest to understand in the case of positive cooperativity. Sharp responses to changes in metabolite concentrations can allow organisms to better respond to environmental changes and maintain metabolic homeostasis. However, despite the fact that negative cooperativity is almost as common as positive, it has been harder to imagine what advantages it provides. Here we use computational models to explore the utility of negative cooperativity in one particular context: that of an inhibitor binding to an enzyme. We identify several factors which may contribute, and show that acting together they can make negative cooperativity advantageous.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 35 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 17%
Researcher 5 14%
Student > Master 5 14%
Student > Bachelor 3 9%
Professor > Associate Professor 3 9%
Other 6 17%
Unknown 7 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 15 43%
Chemistry 5 14%
Agricultural and Biological Sciences 3 9%
Physics and Astronomy 2 6%
Chemical Engineering 1 3%
Other 3 9%
Unknown 6 17%